Inspired by [1]}, [2]}, we implement self-attentions to exploit visual features on top of the baseline, JPoSE [3]} by leveraging the contexts from nouns and verbs of the text query with details in the following section. While we outperform the strong baseline in normalized Discounted Cumulative Gain (nDCG), which is more beneficial for semantic similarity, we fall short in mean Average Precision (mAP), a traditional technique for binary relevance. Our approach is ranked third for nDCG, while it is ranked fourth for mAP. We also analyze various failure examples to save the time of the following researchers on this task.
